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from argparse import ArgumentParser

from tensorrt_llm import BuildConfig
from tensorrt_llm._tensorrt_engine import LLM
from tensorrt_llm.plugin import PluginConfig


def generate_model_engine(model: str, engines_path: str):
    config = BuildConfig(plugin_config=PluginConfig.from_dict({"_gemm_plugin": "auto"}))

    engine = LLM(
        model,
        dtype="float16",
        max_batch_size=128,
        build_config=config,
        guided_decoding_backend="xgrammar",
    )

    engine.save(engines_path)
    engine.shutdown()


if __name__ == "__main__":
    parser = ArgumentParser()
    parser.add_argument(
        "--model", "-m", help="model huggingface id or path to the model"
    )
    parser.add_argument("--engine_path", "-e", help="directory of the output engine")
    FLAGS = parser.parse_args()

    generate_model_engine(FLAGS.model, FLAGS.engine_path)
    print(f"model {FLAGS.model}'s engine has been saved to {FLAGS.engine_path}")
